The present invention relates to the conversion of analog signals to digital signals and more particularly to increasing the dynamic range of analog to digital converters.
A large part of the utility of digital systems is due to their ability to process information derived from physical measurements. One example is a digital receiver that performs demodulation and decoding functions in digital form even though the signal incident on the receiver antenna is in fact an analog signal. Digital audio recording is another example of digital processing that ultimately depends on an analog signal input. Other examples may be found in digital television, instrumentation, hearing aids, etc.
Before digital techniques may be brought to advantage, the signal must be converted from analog form to a series of digital samples. This is the function of an analog to digital converter. A typical analog to digital (A to D or A/D) converter has a continuous analog signal input and outputs new digital samples at fixed intervals as multi-bit words. Each multi-bit word is a value representing the analog signal level at a particular time. The value of the output word typically scales linearly with the input signal level.
The highest value for the multi-bit word, e.g., all 1's, represents a maximum analog signal level measurable by the A to D converter. All 0's then represents a minimum measurable analog signal level. Because the number of bits of the A to D converter output is limited, the digital output value does not represent the analog signal level exactly even when it falls between the minimum and maximum. The error between the analog signal level represented by the actual digital output and the actual analog signal level is referred to as quantization noise. For each A to D converter design, there is a range of analog signal levels over which the converter will measure input signal level accurately. The upper limit of the range is a so called saturation value, the analog signal level corresponding to a digital output of all 1's. The lower limit of this range is defined by the quantization noise level and this level is in turn defined by the number of output bits of the A to D converter. The logarithm of the ratio of the upper and lower limits is sometimes referred to as the dynamic range. Dynamic range is closely related to the number of output bits of the converter and is an important figure of merit for a converter.
As has been mentioned, A to D conversion is important in implementing digital communication systems. Orthogonal frequency division multiplexing (OFDM) is a very useful technique in certain digital communications applications. OFDM is particularly useful in wireless communication systems where signals may take multiple paths from a transmitter to a receiver. OFDM addresses a problem known as multipath caused by differences in delay time among different paths taken from a transmitter to a receiver. The effect of multipath is that data symbols transmitted at different times overlap in their arrival time at the receiver and therefore interfere with one another.
In OFDM, the available bandwidth is divided into subchannels that are orthogonal to one another in the frequency domain. A high data rate signal is effectively transmitted as a set of parallel low data rate signals, each one being carried over a separate subchannel. By creating multiple low data rate subchannels, OFDM lengthens the period occupied by a single symbol so that dispersive effects tend to be confined within the period occupied by transmission of a single symbol, thereby reducing intersymbol interference.
An OFDM transmitter transmits a so-called burst of symbols in a plurality of subchannels simultaneously. To create the transmitted time domain signal corresponding to all of the subchannels, an inverse Fast Fourier Transform (IFFT) is applied to a series of frequency domain symbols to be simultaneously transmitted. The result is a time domain burst of symbols which may be converted to analog form for transmission via the wireless channel. On the receiver end, a received analog signal is converted to digital form by an A to D converter. The digital signal consists of successive bursts of time domain symbols. Each such time domain burst is converted to the frequency domain by use of the Fast Fourier Transform (FFT).
Ideally, the FFT result will be the successive bursts of frequency domain symbols input to the IFFT at the transmitter end. The dynamic range of the A to D converter is very important to accurate reception of the OFDM signal. Consider what happens when the analog signal input to the A to D converter causes saturation of the converter output even momentarily, e.g., for a time corresponding to only one time domain symbol. The digital value for that time domain symbol will now be an error. The error in a single time domain symbol value will however corrupt all of the values in a burst of frequency domain symbols output by the FFT. One solution might be to attenuate the analog signal level input to the A to D converter so that saturation never occurs. Now, however, low time domain symbol values and small differences between time domain symbol values may be lost due to the effects of quantization noise.
One way of providing higher dynamic range is to simply use an A to D converter with a larger number of output bits. For example, one could use a 14 bit converter instead of a 12 bit converter. Converter cost increases sharply with increased bit width of the output word. A 14 bit converter, for example, costs more than twice what a 12 bit converter does.
What is needed are systems and methods for providing A to D converters that have improved dynamic range but with reduced expense as compared to present solutions.